In practical application, it is required to build a mapping relationship between a mobile hotspot and a mobile point of interest (POI), namely, match the mobile hotspot with the mobile POI, and determine mobile POIs respectively corresponding to different mobile hotspots. The mobile POI refers to a transport vehicle having a fixed commuting route, for example, bus Route 112 or subway Line 13.
It is feasible to, based on the above mapping relationship, explore for properties of the transport vehicle such as real-time public transport or real-time subway according to related features or data of the mobile hotspot, determine the user's travel manner through the user's scanning of the mobile hotspot information, and perform user portrait, and so on.
At present, the above mapping relationship is mainly established in the following manners: (1) a specially-assigned person is employed to collect, provide equipment and training, and collect the correspondence relationship between the mobile POI and the mobile hotspot; (2) a crowdsourcing manner is adopted, and a crowdsourcing user uploads the mobile POI corresponding to the mobile hotspot.
However, in the above manner (1), it is required to employ a special person and provide equipment and training, so the implementation cost is high; furthermore, the number of persons employed is limited, and it takes a longer period of time to cover main cities and business districts, so the efficiency is low. In the above manner 2), since crowdsourcing users have different professional qualities, so the accuracy of the uploaded data is hard to ensure.